By Valeri Mladenov, Chrisina Jayne, Lazaros Iliadis
This quantity constitutes the refereed lawsuits of the fifteenth foreign convention on Engineering purposes of Neural Networks, EANN 2014, held in Sofia, Bulgaria, in September 2014. The 18 revised complete papers awarded including five brief papers have been rigorously reviewed and chosen from 37 submissions. The papers show various functions of neural networks and different computational intelligence ways to demanding difficulties proper to society and the financial system. those contain parts equivalent to: environmental engineering, facial features acceptance, category with parallelization algorithms, keep watch over of independent unmanned aerial automobiles, clever shipping, flood forecasting, category of clinical pictures, renewable power structures, intrusion detection, fault type and common engineering.
Read or Download Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings PDF
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Extra info for Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings
Xn (t), . . , xn (T n ), where xn (t) is the observation at time t of the n-th sample timeseries, and T n is the sequence length. A D-dimensional MTS xn is a collection of D univariate timeseries xni (1), . . , xni (T n ) (i = 1, . . , D), such that xni (t) is the observation at time t of the i-th component of the n-th sample MTS. In the following, we use the terms feature and variable to refer to a component of the MTS: each feature i is then associated to a set of univariate timeseries, one for each sample n.
4 Parallelization of SVM According to optimization method applied for SMO, SVM light or QP, the parallelization of SVM is applied on operations of matrices, in contrast with SNN, the parallelization is focused on using of the architecture of the network and some mathematical operations. However, the problem on memory limitations is that our GPU can only store 256 data per block to be parallelized. So, this implies that the maximum number of data in the input are 16. As a consequence, we need to increse the quantity of blocks conﬁgured in the GPU to cover all elements of the database.
Classiﬁcation of Database by Using Parallelization of Algorithms 37 References 1. : Fpga-gpu architecture for kernel svm pedestrian detection. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 61–68. IEEE (2010) 2. : Acceleration of spiking neural networks in emerging multi-core and gpu architectures. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8 (April 2010) 3. : Spiking neural networks.